Automated Computational Design of Composite Li-ion Battery Electrodes Microstructures

复合锂离子电池电极微结构的自动计算设计

• 批准号: 1608058
• 负责人: Soheil Soghrati
• 金额: $ 32万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1608058&HistoricalAwards=false
• 关键词: Automated; Computational; Design; Composite; Li

PI: Soghrati, SoheilProposal Number: 1608058The goal of the proposed research is to develop an integrated computational approach to model and predict the performance of electrodes in Li-ion batteries. The value of understanding and developing models for Li-ion batteries is very significant, since these are very commonly used for energy storage. The proposed research could lead to improved performance for applications in hybrid and electric vehicles, by replacing the currently applied trial and error approach to electrode design. This research will quantify the optimal microstructural features of composite electrodes used in lithium-ion batteries (LIBs) via an integrated computational materials engineering (ICME) approach. While LIBs are the primary technology for secondary energy storage, significant advancements are still needed to improve their power and energy densities for application in the transportation sector. Remarkable improvement in the LIBs performance can be achieved by optimizing the electrode design to maximize the conductivity and surface areas. However, due to inability to realistically model the intricate heterostructure of this composite material, design processes are currently dominated by trial-and-error practices, often leading to sub-optimal designs and significant development time and cost. This proposal aims at overcoming this barrier by developing a new design optimization framework consisting of: (i) extracting hierarchical multiscale imaging data of the electrode microstructure; (ii) developing the ability to automatically create realistic virtual models of the electrode heterostructure based on imaging data; (iii) simulating the multiphysics response of the electrode using an advanced finite element method; (iv) validating the models via experimental testing of coin-cell prototypes; (v) identifying the optimal microstructures that yield highest power and energy densities via a multi-objective genetic algorithm. A hierarchical interface-enriched finite element method (HIFEM) will serve as the main computational engine for simulating the multiphysics behavior of the electrode during charge/discharge cycles. The HIFEM yields the same precision and convergence rate as those of the standard FEM using simple structured meshes that are completely independent of the problem morphology. The HIFEM will be integrated with a virtual prototyping algorithm relying on Non-Uniform Rational Basis Splines (NURBS) to create realistic 3D microstructural models of the composite electrode based on hierarchical imaging data involving x-ray microtomography, focused ion beam tomography, and electron microscopy. To accurately predict the power and energy densities associated with various designs of the electrode, a high fidelity computational model will be implemented to simulate the electro-chemo-mechanical response of the LIB. Furthermore, a fully parallel computing module will be deployed for this automated computational pipeline to both create virtual microstructural models of the electrode and simulate its multiphysics behavior. If successful, the proposed project will lead to the development of a computational framework for the virtual design of composite electrodes. The fundamental knowledge generated through this research will benefit several industries heavily using LIBs in their products, such as the automotive, aerospace, and portable electronics. Moreover, the modeling capabilities that will be developed during the term of this project can be employed for the treatment of a broader range of ICME problems with similar microstructural complexities. To integrate the research, outreach, and education in this project, these tasks will be pursued: (i) strong www and social media presence to facilitate outreach to the public and scientific communities; (ii) participating in K-12 outreach programs organized by Ohio State University (OSU); (iii) training and mentoring of graduate and undergraduate students and integrating research outcomes into the curriculum; (iv) outreach to middle school students via Translating Engineering Research to K-8 program, which will also engage OSU's undergraduate research assistants as career ambassadors.

主要研究者：Soghrati，Soheil提案编号：1608058拟议研究的目标是开发一种集成的计算方法来建模和预测锂离子电池中电极的性能。了解和开发锂离子电池模型的价值非常重要，因为这些电池通常用于储能。拟议的研究可以通过取代目前应用的电极设计试错方法来提高混合动力和电动汽车应用的性能。本研究将通过集成计算材料工程（ICME）方法量化锂离子电池（LIB）中使用的复合电极的最佳微观结构特征。虽然LIB是二次储能的主要技术，但仍需要取得重大进展来提高其功率和能量密度以应用于交通运输领域。通过优化电极设计以最大化电导率和表面积，可以实现LIB性能的显著改善。然而，由于无法真实地模拟这种复合材料的复杂异质结构，设计过程目前主要是试错法，往往导致次优设计和显着的开发时间和成本。该提议旨在通过开发新的设计优化框架来克服该障碍，该设计优化框架包括：（i）提取电极微结构的分级多尺度成像数据;（ii）开发基于成像数据自动创建电极异质结构的真实虚拟模型的能力;（iii）使用先进的有限元方法模拟电极的多物理场响应;（iv）开发基于成像数据自动创建电极异质结构的真实虚拟模型的能力。（iv）通过硬币电池原型的实验测试来验证模型;（v）通过多目标遗传算法来识别产生最高功率和能量密度的最佳微结构。一个分层界面丰富的有限元方法（HIFEM）将作为主要的计算引擎，用于模拟在充电/放电循环过程中的电极的多物理行为。HIFEM产生相同的精度和收敛速度的标准有限元使用简单的结构网格，是完全独立的问题形态。HIFEM将与依赖于非均匀有理基样条（NURBS）的虚拟原型算法集成，以基于涉及X射线显微断层扫描、聚焦离子束断层扫描和电子显微镜的分层成像数据创建复合电极的逼真3D微观结构模型。为了准确预测与电极的各种设计相关的功率和能量密度，将实施高保真度计算模型来模拟LIB的电化学机械响应。此外，一个完全并行的计算模块将被部署到这个自动化的计算管道中，以创建电极的虚拟微观结构模型并模拟其多物理行为。如果成功，该项目将导致开发一个计算框架，用于复合电极的虚拟设计。通过这项研究产生的基础知识将有利于在其产品中大量使用LIB的几个行业，如汽车，航空航天和便携式电子产品。此外，建模能力，将在本项目的期限内开发，可用于治疗更广泛的ICME问题具有类似的微观结构的复杂性。为了在本项目中整合研究、推广和教育，将执行以下任务：（i）强大的www和社交媒体，以促进与公众和科学界的推广;（ii）参与俄亥俄州州立大学（OSU）组织的K-12推广计划;（iii）培训和指导研究生和本科生，并将研究成果纳入课程;（iv）通过将工程研究转化为K-8计划向中学生推广，该计划还将聘请俄勒冈州立大学的本科研究助理担任职业大使。